{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.4. Computing connected components in an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.colors as col\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = np.random.randint(size=(n, n),\n",
    "                        low=0, high=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.grid_2d_graph(n, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_image(img, ax=None, **kwargs):\n",
    "    ax.imshow(img, origin='lower',\n",
    "              interpolation='none',\n",
    "              **kwargs)\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_graph(g, ax=None, **kwargs):\n",
    "    pos = {(i, j): (j, i) for (i, j) in g.nodes()}\n",
    "    node_color = [img[i, j] for (i, j) in g.nodes()]\n",
    "    nx.draw_networkx(g,\n",
    "                     ax=ax,\n",
    "                     pos=pos,\n",
    "                     node_color='w',\n",
    "                     linewidths=3,\n",
    "                     width=2,\n",
    "                     edge_color='w',\n",
    "                     with_labels=False,\n",
    "                     node_size=50,\n",
    "                     **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = plt.cm.Blues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "podoc": {
     "output_text": "<matplotlib.figure.Figure at 0x56bc518>"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W5qO2+VgP8rGPXSJYTtGzoDNuX76kkLFuX77ElZ2vUNt8lvYsinvuWFXIWOvuvMkt3L9C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\nEsESAACAJIIlAAAASQRLAAAAkvw/Laln6ZyeI1wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x56bc518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "show_image(img, ax=ax, cmap=cmap, vmin=-1)\n",
    "show_graph(g, ax=ax, cmap=cmap, vmin=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = g.subgraph(zip(*np.nonzero(img == 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "podoc": {
     "output_text": "<matplotlib.figure.Figure at 0x6e21eb8>"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9fiz+898fYvfgQGzeuD66u2Yfx8mpZpw8ezEOHx314xePyGwBqDPPsfKYLWUSllTqwuXx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\nKcISAACAFGEJAABAirAEAAAg5W8jKHfMMcm2lQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e21eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "show_image(img, ax=ax, cmap=cmap, vmin=-1)\n",
    "show_graph(g2, ax=ax, cmap=cmap, vmin=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components = [np.array(list(comp))\n",
    "              for comp in nx.connected_components(g2)\n",
    "              if len(comp) >= 3]\n",
    "len(components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We copy the image, and assign a new label\n",
    "# to each found component.\n",
    "img_bis = img.copy()\n",
    "for i, comp in enumerate(components):\n",
    "    img_bis[comp[:, 0], comp[:, 1]] = i + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a new discrete color map extending\n",
    "# the previous map with new colors.\n",
    "colors = [cmap(.5), cmap(.75), cmap(1.),\n",
    "          '#f4f235', '#f4a535', '#f44b35',\n",
    "          '#821d10']\n",
    "cmap2 = col.ListedColormap(colors, 'indexed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "podoc": {
     "output_text": "<matplotlib.figure.Figure at 0x6b89f28>"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AACARlgAAACTCEgAAgERYAgAAkAhLAAAAEmEJAABAIiwBAABIhCUAAACJsAQAACARlgAA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\ngS8KVKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b89f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "show_image(img_bis, ax=ax, cmap=cmap2)"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}
